Incremental Multi-graph Matching via Diversity
and Randomness based Graph Clustering
Abstract. Multi-graph matching refers to finding correspondences across
graphs, which are traditionally solved by matching all the graphs in a single batch. However in real-world applications, graphs are often collected
incrementally, rather than once for all. In this paper, we present an incremental multi-graph matching approach, which deals with the arriving
graph utilizing the previous matching results under the global consistency constraint. When a new graph arrives, rather than re-optimizing
over all graphs, we propose to partition graphs into subsets with certain
topological structure and conduct optimization within each subset. The
partitioning procedure is guided by the diversity within partitions and
randomness over iterations, and we present an interpretation showing
why these two factors are essential. The final matching results are calculated over all subsets via an intersection graph. Extensive experimental
results on synthetic and real image datasets show that our algorithm
notably improves the efficiency without sacrificing the accuracy